Ben Fielding: Gensyn’s Polar Opposite Architecture vs AI Data Centers, Fueling an AI-Native Internet, Open vs. Closed Source AI and RL Swarm
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You're now plugged in to the Delphi podcast. Hey, everyone. It's Tommy from Delphi Ventures, and welcome back to the podcast. Today, I'm thrilled to have on Ben who is the cofounder of Jensen Network. Jensen is a decentralized blockchain based compute network that aggregates underutilized hardware into scalable, trustless infra for cost effective deep learning model training. Ben, did ChatchieBT screw up that intro or what? Is is it good? It's,
it's maybe a little bit out of date, but, otherwise, it's reasonably accurate, I would say. Yeah. And, great to be here. Yeah.
I've seen you talk so many times over the years, like, Ethan Ver on podcasts. I have a fond memory of listening to one of your earliest podcasts where you talked about unlocking the compute on laptops and phones to add more electricity to the, quote, unquote, grid of intelligence. Like, unlock that. Always stuck with me. So really glad to have you on.
Nice. Glad to hear it.
So, Ben, you've been I'm not trying to date you, but you've been you've been around for a couple of years, and that's, like, 20 cycles in AI. Right? We started out with with MidJourney, then we had Chacha BT launch, and then centralized is gonna win. And now we have decentralized sort of winning, and now we've open sourced from China. Like, it's just so much volatility for a for an AI founder to question what they're doing, if they're in the right sector, if they're doing the right things. Like, how do you take all that volatility in the AI space and funnel that into Jensen? What are your view
Yeah. It's a it's a good question, and it's one that we, myself and my cofounder, have thought about quite a lot. In general, we made a decision very early on. So we've been going for five years now. And we made a decision really early on to build low level infrastructure technology that was agnostic to the changes and whims and trends of machine learning, both, like, out kind of applied in the world, but also research itself. So I started my research. I started my PhD a decade ago. I saw the rise of deep learning within computer vision. So post AlexNet, all the kind of models that came and we
So there there's something that happens over time when I'm trying to think of, like, path dependency. Right? Like, the last twenty years we've spent or or worse spent optimizing centralized data centers. Right? Having tons of GPUs, they're all collocated, redundant power, fiber, retractable roofs, cooling from the the base, the whole nine yards. Right? And now we're starting to get into this world where something that you're enabling, where any device can can be on that network. Right? Heterogeneous hardware, whatever device, whatever GPU you have, it all adds in. The question I have for you i
Yeah. I I'd say so. I I I think the way I would frame it is a timing piece. So you look at just general distributed systems. Typically, as a pattern in the past, we go through this process of vertical scaling to get the kind of fastest increases in performance, at usually a cost that doesn't matter too much to the market. So you say, okay. I've created this system. Can I scale it up in some way? Can I make it bigger, or can I make it faster? Yes. By just throwing resources at it, but not changing it fundamentally, just kind of scaling it up in place, then that vertical scaling approach is typi
I mean, that is a pretty spicy take, though, in in that if I'm understanding correctly, you're saying that all of the work being done by the hyperscalers, like the Googles, the Metas, the OpenAI's, just massively scaling these data centers is not are
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